A photomask, in general, is used in the intermediate step between the design of an integrated circuit and the actual wafer itself. The photomask acts as a stencil to print an image on the semiconductor material. In the past, the convention was a 1:1 transfer of the image from the mask to the wafer. The 1:1 transfer has given way to a “step and repeat” system utilizing reduction lens systems that expose a mask image stepped many times across the wafer plane. The step and repeat systems result in larger image field sizes on the photomask. Since the magnification is no longer 1:1, the photomask may be referred to as a reticle.
Typically, a computer-aided design (CAD) system is used that enables a designer to completely describe the circuit pattern of an integrated circuit electronically. This electronic design data generates a set of instructions for a pattern generator to use and print the desired mask features onto the photomask. Generally, the mask is then subjected to a variety of processes, which includes etching the pattern into the photomask, and the photomask then becomes ready for quality assurance inspections.
The quality assurance inspections can include, for example, measuring critical dimensions to ensure that the mask features are printed at the proper size. Also, since semiconductor devices are built layer by layer, the image fields of the photomasks used for each layer can be inspected to ensure that the layers “stack” upon each other within some tolerance. In short, defect inspections are performed to ensure that there are no reticle defects larger than a given size. If defects are found, they must be repaired or determined to be within the specification required for printing.
Die-to-die and die-to-database are two pattern defect reticle inspection methods that are known and used in the industry. In die-to-die inspection methods, the patterns in neighboring units are compared in order to detect any discrepancies. Thus, comparing one die against another on the same reticle requires both dice to have the same design. The inspection system scans the areas to be inspected, collects images and processes them in order to identify differences between dice. Differences that exceed a preset threshold level are detected as defects. Since two or more dice with an identical design are needed for die-to-die inspection to work, single die reticles are not capable of being inspected with this method.
In die-to-database inspection methods, the inspection system compares images collected from the reticle to rendered images that are stored in a database. In order for this method to be successful, the rendered images must resemble the processed features on the reticle as closely as possible. Thus, the stored images are rendered from the design data used to write the reticle that is being inspected.
By its own nature, die-to-database inspection is a more complicated process, requiring advanced algorithms for both data rendering, image processing and defect detection. It also requires more processing power. However, one of the great advantages of die-to-database is the ability to inspect single die reticles, and, in general, 100% of any reticle layout. Single die reticles are used for many purposes including reticles for development and debugging of new lithography processes and techniques, multi-product shuttle reticles, and server chip MPU reticles, among others.
Die-to-database photomask inspection requires a calibration step to calibrate the parameters based on which the reference image is rendered. Due to high nonlinearity of the image rendering model, selection of effective calibration samples is crucial for a successful inspection. First-time success rate of die-to-database photomask inspection is also critical. As repeating the inspection procedure may take several hours, first-time failure can severely affect throughputs of customers' products.
One of the main causes for failure in die-to-database inspection is sub-optimal calibration results. Achieving optimal calibration results is often highly dependent on calibration sample selections, which is typically done manually by an operator who must have significant practical experience and/or a deep understanding of the image rendering model in order to make the selection competently. Generally, due to the enormous data, visual inspection of every sample is impractical, and hence the operator typically selects the calibration samples by: a) randomly picking a small set of samples, e.g., tens of samples, from the whole plate of a photomask; and, b) selecting an even smaller set of samples, usually less than 15, from the samples picked in step a) based on various factors. The various factors include representativeness of the samples of the whole plate, uniqueness of the sample patterns, difficulty of image rendering of the sample patterns, and other subjective operator experience.
Despite the knowledge of the operator, calibration samples are usually selected in a subjective and empirical way because there is no scientific evaluation of the process. This can unpredictably cause calibration failure leading to inspection failure. Further, the selection is likely to be incomplete. The operator randomly picks tens of samples from millions of candidates, making it likely for the operator to miss important samples that have large impacts on the calibration. For inexperienced operators or unfamiliar types of photomasks, selecting calibration samples can be time-consuming and challenging.
Therefore, there is a long-felt need for an improved method for selecting effective calibration samples from the photomask design data that increases the first-time success rate. There is also a long-felt need for an automated selection method that replaces part of the manual work to decrease the amount of practical experience required to make a selection. Further, there is a long-felt need for a selection method that selects effective calibration samples in a stable manner.